Customer archive information management method and system

Abstract

The invention discloses a customer archive information management method and system. The system comprises a data acquisition module, an archive analysis module and a customer service module, relates to the technical field of customer archive management, and solves the technical problem that the order conversion rate is not high when business personnel follow up customers due to the fact that the actual transaction intention of the customers is difficult to match with the capabilities of the business personnel in the prior art. The method comprises the following steps: acquiring archive information of a plurality of clients and performance information of business personnel; grouping the plurality of clients based on the transaction intention scores to obtain a client follow-up sequence; and determining a follow-up strategy for the customer based on the customer follow-up sequence and the capability level of the business personnel. According to the method, the clients are grouped according to the transaction intention scores to form an ordered client follow-up sequence, and follow-up tasks are reasonably distributed in combination with the capability levels of the business personnel, so that the matching degree between the business personnel and the clients is enhanced, and the conversion rate and satisfaction of the clients are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of customer file management, and in particular relates to a customer file information management method and system. Background Art

[0002] In the current business environment, traditional customer file management relies primarily on paper forms and manual operations. This management approach presents numerous technical issues, severely restricting the efficiency of grassroots teams and business processing, while also hindering the optimization of enterprise management.

[0003] Most existing customer management solutions utilize CRM and other customer management software to digitally store customer profile information, which has improved customer management efficiency to a certain extent. However, in practice, traditional solutions often categorize and store customer profile information in databases, lacking in-depth analysis of this information. This makes it difficult to align customers' actual transaction intentions with sales personnel's capabilities, resulting in low order conversion rates when sales personnel follow up with customers.

[0004] The present invention provides a customer file information management method and system to solve the above technical problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a customer profile information management method and system for solving the technical problem that the prior art has difficulty in matching the customer's actual transaction intention with the ability of the business personnel, resulting in a low order conversion rate when the business personnel follow up with the customer.

[0006] To achieve the above objectives, the first aspect of the present invention provides a customer profile information management method, comprising:

[0007] Obtain profile information of multiple customers and performance information of each salesperson;

[0008] Analyze the archival information corresponding to each customer to obtain the corresponding customer characteristic data;

[0009] Calculate the customer's transaction intention score based on customer characteristic data;

[0010] Group multiple customers based on transaction intention scores to obtain customer follow-up sequences;

[0011] Determine the capability level of business personnel based on performance information;

[0012] Determine the customer follow-up strategy based on the customer follow-up sequence and the ability level of the salesperson.

[0013] Preferably, the analyzing of the profile information corresponding to each customer includes:

[0014] Extracting each customer's profile information; the profile information includes historical transaction information and historical complaint information; historical transaction information includes several groups of transaction information, transaction records include transaction amount and payment period; historical complaint information includes the number of complaints and complaint type;

[0015] Determine the customer's complaint characteristic data based on the number of complaints; determine the customer's transaction characteristic data based on the order amount; and integrate the complaint characteristic data and transaction characteristic data into customer characteristic data.

[0016] Preferably, the determining of customer complaint characteristic data based on the number of complaints includes:

[0017] Extract the number of complaints, complaint type and complaint time from each customer's historical complaint information;

[0018] Perform linear fitting on the number of complaints in several consecutive periods to obtain the curve of the number of complaints;

[0019] Calculate the first-order derivative function of the complaint frequency change curve to obtain the complaint frequency derivative function; calculate the difference between the maximum function value and the minimum function value of the complaint frequency derivative function, and mark the difference as the complaint frequency change value;

[0020] Determine whether the number of complaints of the same complaint type within a set period is greater than the preset complaint accumulation threshold; if yes, mark the first complaint label as 1; if not, mark the first complaint label as 0;

[0021] Determine whether the interval between adjacent complaint times is less than a preset interval threshold; if yes, mark the second complaint tag as 1; if not, mark the second complaint tag as 0;

[0022] Calculate the sum of the first complaint label and the second complaint label to obtain the complaint label value;

[0023] The complaint frequency change value and complaint label value are integrated into complaint feature data.

[0024] Preferably, the determining of the customer's transaction characteristic data based on the order amount includes:

[0025] Extract the transaction amount from the customer's historical transaction information; where order information includes the customer's transaction amount in several consecutive periods;

[0026] Perform linear fitting on the transaction amounts of several consecutive periods to obtain the transaction amount change curve;

[0027] Calculate the first-order derivative function of the transaction amount change curve to obtain the transaction amount derivative function; calculate the difference between the maximum function value and the minimum function value of the transaction amount derivative function and mark it as transaction feature data.

[0028] Preferably, the step of calculating the customer's transaction intention score based on the customer characteristic data includes:

[0029] Extract complaint feature data and transaction feature data from customer feature data;

[0030] By formula Calculate customer i's transaction intention score CYPi; where TBQi is customer i's complaint label value, TSPi is customer i's complaint frequency change value, and JYTi is customer i's transaction feature data;

[0031] Wherein, a1 and a2 are both proportional coefficients greater than 0; i=1, 2, ..., n, where n is the total number of customers.

[0032] Preferably, grouping multiple customers based on transaction intention scores includes:

[0033] A1: Extract each customer's transaction intention score;

[0034] A2: Determine whether the transaction intention score is greater than the preset upper threshold. If yes, the corresponding customer is placed in the first group. If not, jump to A3.

[0035] A3: Determine whether the transaction intention score is within the preset score range threshold; if yes, the corresponding customer is placed in the second group; if no, the corresponding customer is placed in the third group;

[0036] A4: Integrate the first, second and third groups into a customer follow-up sequence.

[0037] Preferably, determining the capability level of the business personnel based on the performance information includes:

[0038] Obtain performance information for each salesperson; performance information includes sales, customer satisfaction rate, and customer conversion rate for a number of consecutive periods;

[0039] By the formula XNPj=b1×XSEj+e b2×MYLj+b3×ZHLj Calculate the sales capability score XNPj of salesperson j; where XSEj is salesperson j's sales, MYLj is salesperson j's customer satisfaction rate, and ZHLj is salesperson j's customer conversion rate. e is a natural constant; b1, b2, and b3 are all proportional coefficients greater than 0. j = 1, 2, ..., m, where m is the total number of salespersons.

[0040] The sales ability scores are graded based on the preset scoring range to obtain the ability levels; among them, the ability levels include first-level sales, second-level sales and third-level sales.

[0041] Preferably, the grading of sales ability scores based on a preset scoring interval includes:

[0042] The ability level of the corresponding business personnel whose sales ability score is in the first scoring interval is marked as first-level sales; the ability level of the corresponding business personnel whose sales ability score is in the second scoring interval is marked as second-level sales; the ability level of the corresponding business personnel whose sales ability score is in the third scoring interval is marked as third-level sales; among which, the preset scoring intervals include the first scoring interval, the second scoring interval and the third scoring interval.

[0043] Preferably, the determining of the customer follow-up strategy based on the customer follow-up sequence and the capability level of the business personnel includes:

[0044] Extract the customer follow-up sequence and the ability levels of several sales personnel; select sales personnel with the ability level of level one to follow up on the customers in the first group; select sales personnel with the ability level of level two to follow up on the customers in the second group; select sales personnel with the ability level of level three to follow up on the customers in the third group.

[0045] A second aspect of the present invention provides a customer file information management system, comprising: a data collection module, a file analysis module and a customer service module;

[0046] The data collection module is used to obtain the profile information of multiple customers and the performance information of each business person;

[0047] The file analysis module is used to analyze the file information corresponding to each customer to obtain the corresponding customer feature data; calculate the customer's transaction intention score based on the customer feature data; group multiple customers based on the transaction intention score to obtain a customer follow-up sequence;

[0048] The customer service module is used to determine the capability level of the salesperson based on the performance information; and to determine the follow-up strategy for the customer based on the customer follow-up sequence and the capability level of the salesperson.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention obtains customer profile information and salesperson performance information to lay a data foundation for subsequent intelligent analysis; obtains customer feature data through analysis of customer profile information, and then calculates the customer's transaction intention score, thereby realizing a quantitative assessment of customer needs; groups customers according to the transaction intention score to form an orderly customer follow-up sequence, thereby improving the organization and priority control capabilities of customer management; and reasonably allocates follow-up tasks based on the ability level of salespersons, thereby enhancing the matching degree between salespersons and customers, thereby facilitating improved customer conversion rates and satisfaction, and enhancing the company's operational efficiency and market competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 This is an overall flow chart of the customer profile information management method of the present invention;

[0053] Figure 2 Schematic diagram of the principle of the customer file information management system of the present invention. DETAILED DESCRIPTION

[0054] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0055] See also Figure 1-Figure 2 The first embodiment of the present invention provides a customer profile information management method, comprising:

[0056] S1: Obtain the profile information of multiple customers and the performance information of each salesperson;

[0057] S2: Analyze the file information corresponding to each customer to obtain the corresponding customer feature data;

[0058] S3: Calculate the customer's transaction intention score based on customer feature data;

[0059] S4: Group multiple customers based on transaction intention scores to obtain customer follow-up sequences;

[0060] S5: Determine the capability level of business personnel based on performance information;

[0061] S6: Determine the customer follow-up strategy based on the customer follow-up sequence and the ability level of the business personnel.

[0062] In this embodiment, the file information corresponding to each customer is analyzed, including:

[0063] Extracting each customer's profile information; the profile information includes historical transaction information and historical complaint information; historical transaction information includes several groups of transaction information, transaction records include transaction amount and payment period; historical complaint information includes the number of complaints and complaint type;

[0064] Determine the customer's complaint characteristic data based on the number of complaints; determine the customer's transaction characteristic data based on the order amount; and integrate the complaint characteristic data and transaction characteristic data into customer characteristic data.

[0065] In this embodiment, determining the customer's complaint characteristic data based on the number of complaints includes:

[0066] Extract the number of complaints, complaint type and complaint time from each customer's historical complaint information;

[0067] Perform linear fitting on the number of complaints in several consecutive periods to obtain the curve of the number of complaints;

[0068] Calculate the first-order derivative function of the complaint frequency change curve to obtain the complaint frequency derivative function; calculate the difference between the maximum function value and the minimum function value of the complaint frequency derivative function, and mark the difference as the complaint frequency change value;

[0069] Determine whether the number of complaints of the same complaint type within a set period is greater than the preset complaint accumulation threshold; if yes, mark the first complaint label as 1; if not, mark the first complaint label as 0;

[0070] Determine whether the interval between adjacent complaint times is less than a preset interval threshold; if yes, mark the second complaint tag as 1; if not, mark the second complaint tag as 0;

[0071] Calculate the sum of the first complaint label and the second complaint label to obtain the complaint label value;

[0072] The complaint frequency change value and complaint label value are integrated into complaint feature data.

[0073] In this embodiment, determining the customer's transaction characteristic data based on the order amount includes:

[0074] Extract the transaction amount from the customer's historical transaction information; where order information includes the customer's transaction amount in several consecutive periods;

[0075] Perform linear fitting on the transaction amounts of several consecutive periods to obtain the transaction amount change curve;

[0076] Calculate the first-order derivative function of the transaction amount change curve to obtain the transaction amount derivative function; calculate the difference between the maximum function value and the minimum function value of the transaction amount derivative function and mark it as transaction feature data.

[0077] In this embodiment, the customer's transaction intention score is calculated based on the customer characteristic data, including:

[0078] Extract complaint feature data and transaction feature data from customer feature data;

[0079] By formula Calculate customer i's transaction intention score CYPi; where TBQi is customer i's complaint label value, TSPi is customer i's complaint frequency change value, and JYTi is customer i's transaction feature data;

[0080] Wherein, a1 and a2 are both proportional coefficients greater than 0; i=1, 2, ..., n, where n is the total number of customers.

[0081] For example, the proportional coefficients a1=2 and a2=0.5 are set; the complaint label value TBQ1 of customer 1 is 2, the complaint frequency change value TSP1 of customer 1 is 3, and the transaction feature data JYT1 of customer 1 is 18; the transaction intention score CYP1≈4.24 of customer 1 is calculated by the formula.

[0082] In this embodiment, multiple customers are grouped based on transaction intention scores, including:

[0083] A1: Extract each customer's transaction intention score;

[0084] A2: Determine whether the transaction intention score is greater than the preset upper threshold. If yes, the corresponding customer is placed in the first group. If not, jump to A3.

[0085] A3: Determine whether the transaction intention score is within the preset score range threshold; if yes, the corresponding customer is placed in the second group; if no, the corresponding customer is placed in the third group;

[0086] A4: Integrate the first, second and third groups into a customer follow-up sequence.

[0087] For example, the transaction intention score of customer 1 is set to 4.24, the preset score range threshold is [2,5], and the score upper threshold is 15; since the transaction intention score of customer 1 is within the preset score range threshold, customer 1 is classified into the second group.

[0088] In this embodiment, determining the capability level of a business person based on performance information includes:

[0089] Obtain performance information for each salesperson; performance information includes sales, customer satisfaction rate, and customer conversion rate for a number of consecutive periods;

[0090] By the formula XNPj=b1×XSEj+e b2×MYLj+b3×ZHLj Calculate the sales capability score XNPj of salesperson j; where XSEj is salesperson j's sales, MYLj is salesperson j's customer satisfaction rate, and ZHLj is salesperson j's customer conversion rate. e is a natural constant; b1, b2, and b3 are all proportional coefficients greater than 0. j = 1, 2, ..., m, where m is the total number of salespersons.

[0091] The sales ability scores are graded based on the preset scoring range to obtain the ability levels; among them, the ability levels include first-level sales, second-level sales and third-level sales.

[0092] For example, the proportional coefficients b1=0.5, b2=1, and b3=2 are set; the sales of salesperson 1 XSE1=200,000 yuan, the customer satisfaction rate MYL1 of salesperson 1=80%, and the customer conversion rate ZHL1 of salesperson 1=60%; the sales ability score XNP1≈17.39 of salesperson 1 is calculated through the formula.

[0093] In this embodiment, the sales ability scores are graded based on the preset scoring intervals, including:

[0094] The ability level of the corresponding business personnel whose sales ability score is in the first scoring interval is marked as first-level sales; the ability level of the corresponding business personnel whose sales ability score is in the second scoring interval is marked as second-level sales; the ability level of the corresponding business personnel whose sales ability score is in the third scoring interval is marked as third-level sales; among which, the preset scoring intervals include the first scoring interval, the second scoring interval and the third scoring interval.

[0095] For example, the first scoring interval is set to (0, 15], the second scoring interval is set to (15, 30], and the third scoring interval is set to (30, 100]; since the sales ability score of salesperson 1 is in the second scoring interval, the ability level of salesperson 1 is marked as secondary sales.

[0096] In this embodiment, the customer follow-up strategy is determined based on the customer follow-up sequence and the ability level of the business personnel, including:

[0097] Extract the customer follow-up sequence and the ability levels of several sales personnel; select sales personnel with the ability level of level one to follow up on the customers in the first group; select sales personnel with the ability level of level two to follow up on the customers in the second group; select sales personnel with the ability level of level three to follow up on the customers in the third group.

[0098] A second embodiment of the present invention provides a customer file information management system, comprising: a data collection module, a file analysis module and a customer service module;

[0099] Data collection module: used to obtain the archival information of multiple customers and the performance information of each business person;

[0100] Profile Analysis Module: This module analyzes the profile information corresponding to each customer to obtain the corresponding customer characteristic data; calculates the customer's transaction intention score based on the customer characteristic data; and groups multiple customers based on the transaction intention score to obtain a customer follow-up sequence.

[0101] Customer Service Module: used to determine the capability level of sales personnel based on performance information; determine the follow-up strategy for customers based on the customer follow-up sequence and the capability level of sales personnel.

[0102] Some of the data in the above formula are calculated by removing the dimensions and taking their numerical values. The formula is a formula that is closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0103] Working principle of the present invention:

[0104] The present invention obtains the archival information of multiple customers and the performance information of each business person; analyzes the archival information corresponding to each customer to obtain the corresponding customer characteristic data; calculates the customer's transaction intention score based on the customer characteristic data; groups the multiple customers based on the transaction intention score to obtain a customer follow-up sequence; determines the ability level of the business person based on the performance information; and determines the follow-up strategy for the customer based on the customer follow-up sequence and the ability level of the business person.

[0105] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A customer file information management method, characterized in that the steps include: Obtain profile information of multiple customers and performance information of each salesperson; Analyze the archival information corresponding to each customer to obtain the corresponding customer characteristic data; Calculate the customer's transaction intention score based on customer characteristic data; Group multiple customers based on transaction intention scores to obtain customer follow-up sequences; Determine the capability level of business personnel based on performance information; Determine the customer follow-up strategy based on the customer follow-up sequence and the ability level of the salesperson.

2. The customer file information management method according to claim 1, characterized in that: The analysis of the file information corresponding to each customer includes: Extracting each customer's profile information; the profile information includes historical transaction information and historical complaint information; historical transaction information includes several groups of transaction information, transaction records include transaction amount and payment cycle; historical complaint information includes the number of complaints and complaint type; Determine the customer's complaint characteristic data based on the number of complaints; determine the customer's transaction characteristic data based on the order amount; and integrate the complaint characteristic data and transaction characteristic data into customer characteristic data.

3. The customer file information management method according to claim 2, characterized in that: The customer complaint characteristic data determined based on the number of complaints includes: Extract the number of complaints, complaint type and complaint time from each customer's historical complaint information; Perform linear fitting on the number of complaints in several consecutive periods to obtain the curve of the number of complaints; Calculate the first-order derivative function of the complaint frequency change curve to obtain the complaint frequency derivative function; calculate the difference between the maximum function value and the minimum function value of the complaint frequency derivative function, and mark the difference as the complaint frequency change value; Determine whether the number of complaints of the same complaint type within a set period is greater than the preset complaint accumulation threshold; if yes, mark the first complaint label as 1; if not, mark the first complaint label as 0; Determine whether the interval between adjacent complaint times is less than a preset interval threshold; if yes, mark the second complaint tag as 1; if not, mark the second complaint tag as 0; Calculate the sum of the first complaint label and the second complaint label to obtain the complaint label value; The complaint frequency change value and complaint label value are integrated into complaint feature data.

4. The customer file information management method according to claim 2, characterized in that: The transaction characteristic data of the customer determined based on the order amount includes: Extract the transaction amount from the customer's historical transaction information; where order information includes the customer's transaction amount in several consecutive periods; Perform linear fitting on the transaction amounts of several consecutive periods to obtain the transaction amount change curve; Calculate the first-order derivative function of the transaction amount change curve to obtain the transaction amount derivative function; calculate the difference between the maximum function value and the minimum function value of the transaction amount derivative function and mark it as transaction feature data.

5. The customer file information management method according to claim 2, characterized in that: The calculation of the customer's transaction intention score based on the customer characteristic data includes: Extract complaint feature data and transaction feature data from customer feature data; By formula Calculate customer i's transaction intention score CYPi; where TBQi is customer i's complaint label value, TSPi is customer i's complaint frequency change value, and JYTi is customer i's transaction feature data; Wherein, a1 and a2 are both proportional coefficients greater than 0; i=1, 2, ..., n, where n is the total number of customers.

6. The customer file information management method according to claim 1, characterized in that: The grouping of multiple customers based on transaction intention scores includes: A1: Extract each customer's transaction intention score; A2: Determine whether the transaction intention score is greater than the preset upper threshold. If yes, the corresponding customer is placed in the first group. If not, jump to A3. A3: Determine whether the transaction intention score is within the preset score range threshold; if yes, the corresponding customer is placed in the second group; if no, the corresponding customer is placed in the third group; A4: Integrate the first, second and third groups into a customer follow-up sequence.

7. The customer file information management method according to claim 1, characterized in that: Determining the capability level of the business personnel based on the performance information includes: Obtain performance information for each salesperson; performance information includes sales, customer satisfaction rate, and customer conversion rate for a number of consecutive periods; Calculate the sales capability score XNPj of salesperson j based on the linear mapping relationship between sales, customer satisfaction rate, and customer conversion rate; where j = 1, 2, ..., m, where m is the total number of salespersons; The sales ability scores are graded based on the preset scoring range to obtain the ability levels; among them, the ability levels include first-level sales, second-level sales and third-level sales.

8. The customer file information management method according to claim 7, characterized in that: The grading of sales ability scores based on the preset scoring intervals includes: The ability level of the corresponding business personnel whose sales ability score is in the first scoring interval is marked as first-level sales; the ability level of the corresponding business personnel whose sales ability score is in the second scoring interval is marked as second-level sales; the ability level of the corresponding business personnel whose sales ability score is in the third scoring interval is marked as third-level sales; among which, the preset scoring intervals include the first scoring interval, the second scoring interval and the third scoring interval.

9. The customer file information management method according to claim 7, characterized in that: The customer follow-up strategy is determined based on the customer follow-up sequence and the capability level of the business personnel, including: Extract the customer follow-up sequence and the ability levels of several sales personnel; select sales personnel with the ability level of level one to follow up on the customers in the first group; select sales personnel with the ability level of level two to follow up on the customers in the second group; select sales personnel with the ability level of level three to follow up on the customers in the third group.

10. A customer file information management system, used to implement the customer file information management method according to any one of claims 1 to 9, characterized in that: include: Data collection module, archive analysis module and customer service module; The data collection module is used to obtain the profile information of multiple customers and the performance information of each business person; The file analysis module is used to analyze the file information corresponding to each customer to obtain the corresponding customer feature data; calculate the customer's transaction intention score based on the customer feature data; group multiple customers based on the transaction intention score to obtain a customer follow-up sequence; The customer service module is used to determine the capability level of the salesperson based on the performance information; and to determine the follow-up strategy for the customer based on the customer follow-up sequence and the capability level of the salesperson.

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